NSF Postdoctoral Fellowship in Biology FY 2020: Predicting Evolution Through Quantitative Dissection of the Bacterial Proteome

2020 财年 NSF 生物学博士后奖学金：通过细菌蛋白质组的定量解剖预测进化

• 批准号: 2010807
• 负责人: Griffin Chure
• 金额: $ 13.8万
• 依托单位: Chure, Griffin Daniel
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2010807&HistoricalAwards=false
• 关键词: NSF; Postdoctoral; Fellowship; Biology; FY

This action funds an NSF Postdoctoral Research Fellowship in Biology for FY 2020, Integrative Research Investigating the Rules of Life Governing Interactions Between Genomes, Environment and Phenotypes. The fellowship supports research and training of the fellow that will contribute to the area of Rules of Life in innovative ways. This research will use modern technologies to predict how bacteria will evolve in response to environmental pressures, an ability necessary to understand the evolution of dangerous traits such as antibiotic resistance. While an organism’s genetic sequence records its life history, it is difficult to predict how the organism will evolve based on sequence alone. This is due to the inability to predict how mutations can affect the organism’s ability to reproduce, termed its fitness. The project will use recently discovered biological laws to explore how mutations alter the cell’s energy budget to make new proteins. This information will be used to produce a mathematical model to understand how mutations are tied to fitness. The work will develop an experimental system where bacterial evolution can be controlled and predicted, which may have real world applications in medicine and agriculture. This project will augment the fellow’s diverse background through the application of new technology. To broaden the impact of the work, the fellow will train undergraduate and graduate students in the sponsoring scientists’ labs.Recently developed bacterial “growth laws” predict a strong correlation between the cellular growth rate and the proteomic fraction dedicated to ribosomes. This implies that adaptive evolution should skew the allocation of cellular resources towards maintaining ribosomes rather than unnecessary proteins. The objective of this work is to dissect how adaptive mutations modulate the allocation of resources to maximize growth rate, culminating in a mathematical model to predict evolution. The fellow will rely on novel sequencing-based experiments that permit time-resolved measurement of the emergence of novel beneficial mutations in large microbial populations. The effect of these beneficial mutations on the proteomic composition will be monitored via mass spectrometry and RNA-sequencing, illuminating how beneficial mutations alter the allocation of resources. Given a pairwise mapping of the identity of a beneficial mutation and the gene expression profile, the fellow aims to quantitatively map the fitness landscape. It then becomes possible to predict how changes in gene expression lead to changes in fitness. The fellow’s background in modeling and experimentation will be strengthened by the development and application of sequencing-based experiments. The broader impacts will include the training of undergraduate and graduate students in the design, execution, and interpretation of project experiments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项行动资助了2020财年的NSF生物学博士后研究奖学金，综合研究调查了基因组，环境和表型之间的生命相互作用的规则。该研究金支持研究员的研究和培训，以创新的方式为生活规则领域做出贡献。这项研究将使用现代技术来预测细菌如何应对环境压力，这是了解抗生素耐药性等危险性状进化所必需的能力。 虽然生物体的基因序列记录了它的生活史，但很难仅仅根据序列来预测生物体将如何进化。这是由于无法预测突变如何影响生物体的繁殖能力，称为其适应性。 该项目将利用最近发现的生物学定律来探索突变如何改变细胞的能量预算以制造新的蛋白质。这些信息将被用来产生一个数学模型，以了解突变如何与适应性联系在一起。这项工作将开发一个可以控制和预测细菌进化的实验系统，这可能在医学和农业方面有真实的世界应用。这个项目将通过新技术的应用增加研究员的多样化背景。为了扩大这项工作的影响，该研究员将在赞助科学家的实验室中培训本科生和研究生。最近开发的细菌“生长定律”预测细胞生长速率与核糖体专用的蛋白质组分数之间存在很强的相关性。这意味着适应性进化应该将细胞资源的分配转向维持核糖体，而不是不必要的蛋白质。这项工作的目的是剖析适应性突变如何调节资源分配以最大限度地提高增长率，最终建立一个数学模型来预测进化。该研究员将依赖于新的基于测序的实验，这些实验允许对大型微生物种群中新的有益突变的出现进行时间分辨测量。这些有益突变对蛋白质组组成的影响将通过质谱和RNA测序进行监测，阐明有益突变如何改变资源分配。给定有益突变的身份和基因表达谱的成对映射，该研究员的目标是定量映射适应度景观。 然后就有可能预测基因表达的变化如何导致适应性的变化。该研究员在建模和实验的背景将通过基于测序的实验的开发和应用得到加强。更广泛的影响将包括本科生和研究生在项目实验的设计、执行和解释方面的培训。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

hplc-py: A Python Utility For Rapid Quantification ofComplex Chemical Chromatograms

hplc-py：用于快速定量复杂化学色谱图的 Python 实用程序

• DOI: 10.21105/joss.06270
• 发表时间: 2024
• 期刊: Journal of Open Source Software
• 作者: Chure, Griffin;Cremer, Jonas
• 通讯作者: Cremer, Jonas